Abstract
This paper presents a novel hybrid intelligent system which synergizes the concept of knowledge reduction in rough set theory with the human-like reasoning style of fuzzy systems and the learning and connectionist structure of neural networks. The proposed rough set-based neuro-fuzzy system (RNFS) incorporates a wrapper-based feature selection method that employs the mutual information maximization scheme which selects attributes with high relevance and the concept of knowledge reduction in rough set theory which selects attributes with low redundancy. Experimental results show that the proposed RNFS utilizes less computational effort and yielded promising results on feature selection as well as classification accuracy.
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